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Beijing, with the highest number of motor vehicles in China, significantly contributes to O3 pollution through substantial NOx and VOC emissions in the on-road transportation sector. Understanding the unique impact of emissions from different vehicle types on O3 levels is crucial for developing targeted strategies for O3 pollution. This study applied the Community Multiscale Air Quality Modeling System (CMAQ) to comprehensively investigate the impacts of emissions from different vehicle types on O3 levels in various regions of Beijing and to provide valuable insights into source contributions and formation processes. The results revealed that various vehicle types exhibited different spatial-temporal emission patterns, with medium-heavy duty trucks (HDT) and mini-light passenger vehicles (LDPV) identified as the primary contributors to NOx (36.1 %) and VOC (57.6 %) emissions. Using the Integrated Source Apportionment Method (ISAM) coupled in CMAQ, we found the total vehicle emissions contributed to over 20 % of daily maximum 8–h average O3 (MDA8 O3) concentration, ranked as the second largest contributor after regional transport. Contributions to O3 formation from LDPV and medium-large passenger vehicles (MDPV) were 2.6–4.0 and 4.2–6.8 ppb and mainly concentrated in urban areas, while the contributions from mini-light duty trucks (LDT) and HDT were 3.5–4.8 and 3.7–6.2 ppb and mainly concentrated in suburban areas. Through scenario analysis that removed emissions from specific types of vehicles, we found removing LDPV emissions led to decreases in daytime O3 concentration by 0.3–3.8 ppb. In contrast, removing MDPV emissions led to notable O3 increases by 4.0–11.8 ppb at rush hours. Removing LDT and HDT emissions resulted in 0.6–8.0 ppb increases in nocturnal O3 concentrations while 0.8–2.0 ppb decreases during the afternoon. This research highlights the necessity of tailoring control strategies for different vehicle types to effectively reduce O3 levels in Beijing and provides useful information for decision-makers to formulate effective measures of vehicle management in the future.

Abstract This study explored the relationship between parenting stress, parenting style, parenting quality, and young children's executive function. In total, 243 firstborns aged 2–9 years old (SD = 3.82) and their parents from two-child families in Beijing participated in the study, which used executive function tasks and parenting questionnaires. The results found that (1) parenting stress negatively predicted parenting quality; (2) parenting style partially mediated the relationship between parenting stress and parenting quality; (3) children's executive function partially mediated the relationship between parenting stress and parenting quality; and (4) the spoiled, democratic, permissive, and authoritarian parenting styles each play a chain mediating role with young children's executive function between parenting stress and parenting quality. Taken together, these findings provide implications for scientific parenting of children with different psychological characteristics (such as executive function) in multiple-child families under Parenting stress.

In developing countries, especially China, large-scale commercial complexes are the current trend in commercial real estate. Compared with other similar buildings, scientific site selection is very important for the smooth construction and efficient operation of these complexes. However, there is still a lack of a targeted evaluation index system and quantitative evaluation methods. Therefore, this paper put forward the evaluation index system and method of large-scale commercial-complex location based on a projection pursuit model. First, this paper comprehensively considered the environmental, social, and economic factors, and used secondary and tertiary indicators to systematically establish an evaluation index system. This index system effectively dealt with the complex problem of its site selection. Compared with the traditional multi-attribute evaluation method based on expert advice, the evaluation method of the improved projection pursuit model based on a sparrow search algorithm constructed in this paper was to mine key information from the evaluation data, which could evaluate the site suitability of large commercial complexes more scientifically and objectively. In addition, this paper made a detailed empirical study of the Joy City project in Nanchang, China. The research results found the key factors affecting the site selection of the project and determined that the site-selection evaluation result of the project was medium. The research results of this paper provide the scientific and objective decision-making basis for the development enterprises of large commercial complexes to reduce site-selection risk and improve investment efficiency.

Abstract Brain metastasis (BM) frequently occurs in advanced non-small cell lung cancer (NSCLC) and is associated with poor clinical prognosis. Due to the location of metastatic lesions, the surgical resection is limited and the chemotherapy is ineffective because of the existence of the blood brain barrier (BBB). Therefore, it is essential to enhance our understanding about the underlying mechanisms associated with brain metastasis in NSCLC. In the present study, we explored the RNA-Seq data of brain metastasis cells from the GEO database, and extracted RNA collected from primary NSCLC tumors as well as paired brain metastatic lesions followed by microRNA PCR array. Meanwhile, we improved the in vivo model and constructed a cancer stem cell-derived transplantation model of brain metastasis in mice. Our data indicated that the level of miR-596-3p is high in primary NSCLC tumors, but significantly downregulated in the brain metastatic lesion. The prediction target of microRNA suggested that miR-596-3p was considered to modulate two genes essential in the brain invasion process, YAP1 and IL-8 that restrain the invasion of cancer cells and permeability of BBB, respectively. Moreover, in vivo experiments suggested that our model mimics the clinical aspect of NSCLC and improves the success ratio of brain metastasis model. The results demonstrated that miR-596-3p significantly inhibited the capacity of NSCLC cells to metastasize to the brain. Furthermore, these finding elucidated that miR-596-3p exerts a critical role in brain metastasis of NSCLC by modulating the YAP1-IL8 network, and this miRNA axis may provide a potential therapeutic strategy for brain metastasis.

Background: Low birthweight attributable to fine particulate matter (PM2.5) exposure is a global issue affecting infant health, especially in low- and middle-income countries (LMICs). However, large-population studies of multiple LMICs are lacking, and little is known about whether the source of PM2.5 is a determinant of the toxic effect on birthweight. Objective: We examined the effect on birthweight of long-term exposure to PM2.5 from different sources in LMICs. Methods: The birthweights of 53,449 infants born between September 16, 2017 and September 15, 2018 in 17 LMICs were collected from demographic and health surveys. Long-term exposure to PM2.5 in 2017 produced by 20 different sources was estimated by combining chemical transport model simulations with satellite-based concentrations of total mass. Generalized linear regression models were used to investigate the associations between birthweight and each source-specific PM2.5 exposure. A multiple-pollutant model with a ridge penalty on the coefficients of all 20-source-specific components was employed to develop a joint exposure–response function (JERF) of the PM2.5 mixtures. The estimated JERF was then used to quantify the global burden of birthweight reduction attributable to PM2.5 mixtures and to PM2.5 from specific sources. Results: The fully adjusted single-pollutant model indicated that exposure to a 10 μg/m3 increase in total PM2.5 was significantly associated with a −6.6 g (95% CI –11.0 to –2.3) reduction in birthweight. In single- and multiple-pollutant models, significant birthweight changes were associated with exposure to PM2.5 produced by international shipping (SHP), solvents (SLV), agricultural waste burning (GFEDagburn), road transportation (ROAD), waste handling and disposal (WST), and windblown dust (WDUST). Based on the global average exposure to PM2.5 mixtures, the JERF showed that the overall change in birthweight could mostly be attributed to PM2.5 produced by ROAD (–37.7 g [95% CI –49.2 to –24.4] for a global average exposure of 2.2 μg/m3), followed by WST (–27.5 g [95% CI –42.6 to –10.7] for a 1.6-μg/m3 exposure), WDUST (–19.5 g [95% CI –26.7 to –12.6] for a 8.6-μg/m3 exposure), and SHP (–19.0 g [95% CI –32.3 to –5.7] for a 0.2-μg/m3 exposure), which, with the exception of WDUST, are anthropogenic sources. The changes in birthweight varied geographically and were co-determined by the concentration as well as the source profile of the PM2.5 mixture. Conclusion: PM2.5 exposure is associated with a reduction in birthweight, but our study shows that the magnitude of the association differs depending on the PM2.5 source. A source-targeted emission-control strategy that considers local features is therefore critical to maximize the health benefits of air quality improvement, especially with respect to promoting maternal and child health.

Previous studies have demonstrated an association between short-term exposure to ambient temperature and mortality. However, the long-term effects of elevated temperature and temperature variability on mortality have remained somewhat elusive in epidemiological studies. We conducted a comprehensive epidemiological study utilizing Chinese population census data from 2000 and 2010. Census-derived demographic and socioeconomic factors were paired with temperature data from the European Re-Analysis Land Dataset across 2823 counties. We employed a difference-in-difference approach to quantitatively examine the relationship between all-cause mortality and annual exposure to mean temperature and diurnal temperature range (DTR). Additionally, we evaluated the potential effects of socioeconomic and environmental covariate modifications on this relationship and calculated the attributable mortality. Lastly, we projected excess deaths attributable to annual temperature exposure under various shared socioeconomic pathways (SSPs, e.g. SSP126, SSP370, and SSP585). For each 1 °C rise in annual mean temperature and DTR, the mortality risk could increase by 6.12% (95% CI: 0.84%, 11.69%) and 7.72% (95% CI: 3.75%, 11.84%), respectively. Counties with high labor-force ratios and high NO _2 and O _3 concentrations appeared to be sensitive to the annual mean temperature and DTR. Climate warming from 2000 to 2010 may have resulted in 5.85 and 14.46 additional deaths per 10 000 people attributable to changes in annual mean temperature and DTR, respectively. The excess mortality related to changes in annual mean temperature and DTR is expected to increase in the future, with special attention warranted for long-term temperature changes in Southwest China. Our findings indicate that long-term mean temperature and DTR could significantly impact mortality rates. Given the spatial heterogeneity of increased mortality risk, the formulation of region-specific strategies to tackle climate change is crucial.

Summary: An increasing number of patients are being killed by coronavirus disease 2019 (COVID-19), however, risk factors for the fatality of COVID-19 remain unclear. A total of 21,392 COVID-19 cases were recruited in the Hubei Province of China between December 2019 and February 2020, and followed up until March 18, 2020. We adopted Cox regression models to investigate the risk factors for case fatality and predicted the death probability under specific combinations of key predictors. Among the 21,392 patients, 1,020 (4.77%) died of COVID-19. Multivariable analyses showed that factors, including age (≥60 versus 39°C versus 10 × 109/L versus (4–10) × 109/L, HR = 1.69; 95% CI, 1.35, 2.13), and lymphocyte counts (

Although increasing evidence has reported that unfavorable temperature may lead to increased premature mortality, a systematic assessment is lacking on the impact of ambient temperature on years of life lost (YLL) and life expectancy in China. Daily data on mortality, YLL, meteorological factors and air pollution were obtained from 93 Chinese cities during 2013–2016. A two-stage analytic approach was applied for statistical analysis. At the first stage, a distributed lag non-linear model with a Gaussian link was used to estimate the city-specific association between ambient temperature and YLLs. At the second stage, a meta-analysis was used to obtain the effect estimates at regional and national levels. We further estimated the corresponding YLLs and average life expectancy loss per deceased person attributable to the non-optimum temperature exposures based on the established associations. We observed ‘U’ or ‘J’ shaped associations between daily temperature and YLL. The heat effect appeared on the current day and lasted for only a few days, while the cold effect appeared a few days later and lasted for longer. In general, 6.90% (95% confidence interval (CI): 4.62%, 9.18%) of YLLs could be attributed to non-optimum temperatures at the national level, with differences across different regions, ranging from 5.36% (95% CI: −3.36%, 6.89%) in east region to 9.09% (95% CI: −5.55%, 23.73%) in northwest region. For each deceased person, we estimated that non-optimum temperature could cause a national-averaged 1.02 years (95% CI: 0.68, 1.36) of life loss, with a significantly higher effect due to cold exposure (0.89, 95% CI: 0.59, 1.19) than that of hot exposure (0.13, 95% CI: 0.09, 0.16). This national study provides evidence that both cold and hot weather might result in significant YLL and lower life expectancy. Regional adaptive policies and interventions should be considered to reduce the mortality burden associated with the non-optimum temperature exposures.

At present, there is still a lack of studies to address the dynamics underlying epidemics of bacillary dysentery (BD), with particular concern on the role of climatic drivers across different regions of a country or the world. The variability of climate domains, non-linear interactions, and covariations of climatic variables pose challenges for explaining the correlation between environment and BD and identifying causal climatic drivers. In this nationwide study involving 31 provincial capital cities in China, we used the empirical dynamic modeling (EDM), which is a framework for nonlinear time series analysis, to explore climate-driven patterns of BD. We first identified possible temperature (i.e. mostly via its seasonality) and relative humidity driving BD dynamics nationally. Then, we used the EDM to estimate the causal intensity of temperature and relative humidity in different latitudes. The results reveal that the combined nonlinear effect of them on BD may be nationwide, but this effect is concealed due to their high correlation in northern regions. We also found an approximately S-shaped relationship between temperature and BD at the population level; while the effects of relative humidity on BD are strongly dependent on environmental details, especially at temperatures above 0 °C. Temperature may potentially mediate and affect the effects of relative humidity on BD. This nationwide study provides a unified explanation for causal climate drivers of BD, regardless of the different climatic domains and epidemic patterns of BD in diverse cities.